import torch
import torch.nn as nn


# 定义PixelCNN++模型
class PixelCNN(nn.Module):
    def __init__(self, num_channels=64, num_layers=5):
        super(PixelCNN, self).__init__()
        self.num_channels = num_channels
        self.num_layers = num_layers
        self.layers = nn.ModuleList()

        # 输入卷积层
        self.layers.append(nn.Conv2d(3, num_channels, kernel_size=7, padding=3))

        # 中间卷积层
        for _ in range(num_layers):
            self.layers.append(nn.Conv2d(num_channels, num_channels, kernel_size=3, padding=1))
            self.layers.append(nn.ReLU())

        # 输出卷积层
        self.layers.append(nn.Conv2d(num_channels, 256, kernel_size=1))

    def forward(self, x):
        for layer in self.layers:
            x = layer(x)
        return x


# 初始化PixelCNN模型
pixel_cnn = PixelCNN()


# 定义扩散过程
def diffusion_process(initial_sample, num_steps, noise_scale):
    current_sample = initial_sample.clone()

    for step in range(num_steps):
        # 生成噪声
        noise = torch.randn_like(current_sample) * noise_scale
        # 添加噪声到当前样本
        noisy_sample = current_sample + noise
        # 使用PixelCNN生成下一步的样本
        next_sample_logits = pixel_cnn(noisy_sample)
        # 将logits转换为概率分布
        next_sample_probs = torch.softmax(next_sample_logits, dim=1)
        # 从概率分布中采样下一步的样本
        next_sample = torch.multinomial(next_sample_probs[:, :, :, :], 1)
        # 更新当前样本
        current_sample = next_sample.float()

    return current_sample


# 使用扩散过程生成样本
initial_sample = torch.randn(1, 3, 64, 64)  # 初始样本
num_steps = 100  # 扩散步数
noise_scale = 0.1  # 噪声标准差

final_sample = diffusion_process(initial_sample, num_steps, noise_scale)
